Enterprises face a growing threat from deepfake-enabled interview fraud, where bad actors use real-time face swapping and voice cloning to impersonate candidates during remote hiring processes.
New research goes beyond behavioral analysis to trace the internal mechanisms LLMs use when weighing competing reward signals, offering insights into AI decision-making at the circuit level.
Researchers introduce a new benchmark for evaluating how general LLM agents perform when given additional compute resources at inference time, addressing a critical gap in agent evaluation.
New research proposes combining ML-assisted sampling with LLM labeling to measure policy-violating content at scale, offering a methodological breakthrough for detecting synthetic media and deepfakes.
Anthropic launches secondary share sale worth up to $6B, valuing the Claude maker at $61B as it competes with OpenAI for AI dominance and attracts major backing from Google and Salesforce.
Moving beyond simple accuracy, these five metrics—task success rate, tool usage accuracy, context coherence, response latency, and safety compliance—reveal what truly matters when assessing AI agents.
Rising deepfake fraud incidents are creating new investment opportunities in cybersecurity ETFs as detection and authentication technologies become critical enterprise priorities.
From ViT to Swin Transformer, these four architectures revolutionized how AI processes visual information—and they're the backbone of today's deepfake generators and detectors alike.
Startup Taalas is challenging GPU dominance with hardwired AI chips designed specifically for inference, claiming 17,000 tokens per second throughput for ubiquitous AI deployment.
New research introduces ADAPT, a hybrid optimization technique that combines discrete and continuous methods to visualize and understand internal features of large language models.
New JAX-based library enables differentially private training of machine learning models, addressing critical privacy concerns in AI development and synthetic media generation.
New research introduces proxy methods that preserve gradient influence signals while dramatically reducing computational costs for selecting optimal training data in large language model fine-tuning.